Upload README.md

README.md

@@ -10,6 +10,7 @@ tags:
 - intel-autoround
 - awq
 - autoawq
+- auto-awq
 - woq
 license: apache-2.0
 model_name: Pythia 12b
@@ -24,8 +25,6 @@ prompt_template: '{prompt}
 quantized_by: fbaldassarri
 ---
 
-
-
 ## Model Information
 
 Quantized version of [EleutherAI/pythia-12b](https://huggingface.co/EleutherAI/pythia-12b) using torch.float32 for quantization tuning.
@@ -34,7 +33,7 @@ Quantized version of [EleutherAI/pythia-12b](https://huggingface.co/EleutherAI/p
 - Asymmetrical Quantization
 - Method AutoAWQ
 
-Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
+Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.2 
 
 Note: this INT4 version of pythia-12b has been quantized to run inference through CPU.
 
@@ -48,13 +47,14 @@ I suggest to install requirements into a dedicated python-virtualenv or a conda
 python -m pip install <package> --upgrade
 ```
 
-- accelerate==1.0.1
+- accelerate==1.2.0
+- autoawq==0.2.7.post3
 - auto_gptq==0.7.1
-- neural_compressor==3.1
-- torch==2.3.0+cpu
-- torchaudio==2.5.0+cpu
-- torchvision==0.18.0+cpu
-- transformers==4.45.2
+- neural_compressor==3.1.1
+- torch==2.4.1+cpu
+- torchaudio==2.4.1+cpu
+- torchvision==0.19.1+cpu
+- transformers==4.47.0
 
 ### Step 2 Build Intel Autoround wheel from sources
 
@@ -70,8 +70,8 @@ python -m pip install git+https://github.com/intel/auto-round.git
   model = AutoModelForCausalLM.from_pretrained(model_name)
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   from auto_round import AutoRound
-  bits, group_size, sym = 4, 128, False
-  autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
+  bits, group_size, sym, device, amp = 4, 128, False, 'cpu', False
+  autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp)
   autoround.quantize()
   output_dir = "./AutoRound/EleutherAI_pythia-12b-autoawq-int4-gs128-asym"
   autoround.save_quantized(output_dir, format='auto_awq', inplace=True)